Abstract: Topological evolution of a lesion within a time series of medical imagery is provided. In various embodiments, a time series of medical images is read. Each of the images depicts a subject anatomy and a lesion. The lesion has a size and a contour within each of the medical images. At least one anatomical label is read for the subject anatomy within each of the plurality of images. Based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, a further contour of the lesion is predicted outside of the time series.

Abstract: Medical imaging device- and display-invariant segmentation and measurement is provided. In various embodiments, a plurality of medical images is read from a data store. Metadata of each of the plurality of medical images is read. The metadata identifies an image acquisition device associated with each of the plurality of medical images. Based on the plurality of medical images and the metadata of each of the plurality of images, a learning system is trained to determine one or more image correction parameters. The one or more image correction parameters optimize segmentation of the plurality of medical images.

Abstract: Topological evolution of a lesion within a time series of medical imagery is provided. In various embodiments, a time series of medical images is read. Each of the images depicts a subject anatomy and a lesion. The lesion has a size and a contour within each of the medical images. At least one anatomical label is read for the subject anatomy within each of the plurality of images. Based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, a further contour of the lesion is predicted outside of the time series.

Abstract: Topological evolution of a lesion within a time series of medical imagery is provided. In various embodiments, a time series of medical images is read. Each of the images depicts a subject anatomy and a lesion. The lesion has a size and a contour within each of the medical images. At least one anatomical label is read for the subject anatomy within each of the plurality of images. Based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, a further contour of the lesion is predicted outside of the time series.

Abstract: Evaluation of segmentation of medical imagery is provided. In various embodiments, a candidate segmentation of a medical image of an anatomical feature is received. The candidate segmentation is provided to a first trained classifier. An indication is received from the first trained classifier of the accuracy of the candidate segmentation based on one or more feature of the candidate segmentation. One or more prior segmentation of a prior medical image of the anatomical feature is received. The candidate segmentation and the one or more prior segmentation are provided to a second trained classifier. An indication is received from the second trained classifier of the accuracy of the candidate segmentation based on one or more feature of the one or more prior segmentation.

Abstract: Topological evolution of a lesion within a time series of medical imagery is provided. In various embodiments, a time series of medical images is read. Each of the images depicts a subject anatomy and a lesion. The lesion has a size and a contour within each of the medical images. At least one anatomical label is read for the subject anatomy within each of the plurality of images. Based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, a further contour of the lesion is predicted outside of the time series.

Abstract: Topological evolution of a lesion within a time series of medical imagery is provided. In various embodiments, a time series of medical images is read. Each of the images depicts a subject anatomy and a lesion. The lesion has a size and a contour within each of the medical images. At least one anatomical label is read for the subject anatomy within each of the plurality of images. Based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, a further contour of the lesion is predicted outside of the time series.

Abstract: Medical imaging device- and display-invariant segmentation and measurement is provided. In various embodiments, a plurality of medical images is read from a data store. Metadata of each of the plurality of medical images is read. The metadata identifies an image acquisition device associated with each of the plurality of medical images. Based on the plurality of medical images and the metadata of each of the plurality of images, a learning system is trained to determine one or more image correction parameters. The one or more image correction parameters optimize segmentation of the plurality of medical images.

Abstract: Evaluation of segmentation of medical imagery is provided. In various embodiments, a candidate segmentation of a medical image of an anatomical feature is received. The candidate segmentation is provided to a first trained classifier. An indication is received from the first trained classifier of the accuracy of the candidate segmentation based on one or more feature of the candidate segmentation. One or more prior segmentation of a prior medical image of the anatomical feature is received. The candidate segmentation and the one or more prior segmentation are provided to a second trained classifier. An indication is received from the second trained classifier of the accuracy of the candidate segmentation based on one or more feature of the one or more prior segmentation.

Abstract: Multiple algorithm lesion segmentation using an atlas is provided. In various embodiments, a plurality of medical images are read from an image repository. Each of the plurality of medical images has a source modality. Each of the plurality of medical images is registered to an anatomical atlas. An anatomical region depicted in each of the plurality of medical images is determined thereby. Based upon the source modality and the anatomical region depicted in each of the plurality of medical images, one of a plurality of segmentation algorithms is selected for each of the plurality of medical images. The selected segmentation algorithms are applied to each of the plurality of medical images. The results of the selected segmentation algorithms are displayed.

Abstract: Topological evolution of a lesion within a time series of medical imagery is provided. In various embodiments, a time series of medical images is read. Each of the images depicts a subject anatomy and a lesion. The lesion has a size and a contour within each of the medical images. At least one anatomical label is read for the subject anatomy within each of the plurality of images. Based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, a further contour of the lesion is predicted outside of the time series.

Abstract: Multiple algorithm lesion segmentation using an atlas is provided. In various embodiments, a plurality of medical images are read from an image repository. Each of the plurality of medical images has a source modality. Each of the plurality of medical images is registered to an anatomical atlas. An anatomical region depicted in each of the plurality of medical images is determined thereby. Based upon the source modality and the anatomical region depicted in each of the plurality of medical images, one of a plurality of segmentation algorithms is selected for each of the plurality of medical images. The selected segmentation algorithms are applied to each of the plurality of medical images. The results of the selected segmentation algorithms are displayed.

Abstract: The present invention is an automated image analysis framework for cervical cancerous lesion detection. The present invention uses domain-specific diagnostic features in a probabilistic manner using conditional random fields. In addition, the present invention discloses a novel window-based performance assessment scheme for two-dimensional image analysis, which addresses the intrinsic problem of image misalignment. As a domain-specific anatomical feature, image regions corresponding to different tissue types are extracted from cervical images taken before and after the application of acetic acid during a clinical exam. The unique optical properties of each tissue type and the diagnostic relationships between neighboring regions are incorporated in the conditional random field model. The output provides information about both the tissue severity and the location of cancerous tissue in an image.

Abstract: A process and device for detecting colon cancer by classifying and annotating clinical features in video data containing colonoscopic features by applying a probabilistic analysis to intra-frame and inter-frame relationships between colonoscopic features in spatially and temporally neighboring portions of video frames, and classifying and annotating as clinical features any of the colonoscopic features that satisfy the probabilistic analysis as clinical features. Preferably the probabilistic analysis is Hidden Markove Model analysis, and the process is carried out by a computer trained using semi supervised learning from labeled and unlabeled examples of clinical features in video containing colonoscopic features.

Abstract: The present invention is an automated image analysis framework for cervical cancerous lesion detection. The present invention uses domain-specific diagnostic features in a probabilistic manner using conditional random fields. In addition, the present invention discloses a novel window-based performance assessment scheme for two-dimensional image analysis, which addresses the intrinsic problem of image misalignment. As a domain-specific anatomical feature, image regions corresponding to different tissue types are extracted from cervical images taken before and after the application of acetic acid during a clinical exam. The unique optical properties of each tissue type and the diagnostic relationships between neighboring regions are incorporated in the conditional random field model. The output provides information about both the tissue severity and the location of cancerous tissue in an image.